diff --git a/src/animatediff/generate.py b/src/animatediff/generate.py
index a7e223b8..64c04973 100644
--- a/src/animatediff/generate.py
+++ b/src/animatediff/generate.py
@@ -19,6 +19,7 @@
                        EulerDiscreteScheduler,
                        StableDiffusionControlNetImg2ImgPipeline,
                        StableDiffusionPipeline, StableDiffusionXLPipeline)
+from diffusers.loaders import UNet2DConditionLoadersMixin
 from PIL import Image
 from torchvision.datasets.folder import IMG_EXTENSIONS
 from tqdm.rich import tqdm
@@ -71,7 +72,8 @@
     "qr_code_monster_v1" : ['monster-labs/control_v1p_sd15_qrcode_monster'],
     "qr_code_monster_v2" : ['monster-labs/control_v1p_sd15_qrcode_monster', 'v2'],
     "controlnet_mediapipe_face" : ['CrucibleAI/ControlNetMediaPipeFace', "diffusion_sd15"],
-    "animatediff_controlnet" : [None, "data/models/controlnet/animatediff_controlnet/controlnet_checkpoint.ckpt"]
+    "animatediff_controlnet" : [None, "data/models/controlnet/animatediff_controlnet/controlnet_checkpoint.ckpt"],
+    "controlnet_loose" : ["data/models/controlnet/controlnet_loose/pytorch_lora_weights.safetensors"]
 }
 
 # Edit this table if you want to change to another controlnet checkpoint
@@ -118,7 +120,17 @@
     onnxruntime_installed = False
 
 
-
+def attach_loaders_mixin(model):
+    # hacky way to make ControlNet work with LoRA. This may not be required in future versions of diffusers.
+    model.text_encoder_name = "text_encoder"
+    model.unet_name = "unet"
+    # mixin_instance = UNet2DConditionLoadersMixin()
+    for attr_name, attr_value in vars(UNet2DConditionLoadersMixin).items():
+        # print(attr_name)
+        if callable(attr_value):
+            # setattr(model, attr_name, functools.partialmethod(attr_value, model).__get__(model, model.__class__))
+            setattr(model, attr_name, partial(attr_value, model))
+    return model
 
 logger = logging.getLogger(__name__)
 
@@ -322,7 +334,13 @@ def create_controlnet_model(pipe, type_str, is_sdxl):
             addr = controlnet_address_table[type_str]
             if addr[0] != None:
                 if len(addr) == 1:
-                    return ControlNetModel.from_pretrained(addr[0], torch_dtype=torch.float16)
+                    if type_str == "controlnet_loose":
+                        cn = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16)
+                        cn = attach_loaders_mixin(cn)
+                        cn.load_attn_procs(addr[0])
+                        return cn
+                    else:
+                        return ControlNetModel.from_pretrained(addr[0], torch_dtype=torch.float16)
                 else:
                     return ControlNetModel.from_pretrained(addr[0], subfolder=addr[1], torch_dtype=torch.float16)
             else: